Statistical learning is a mechanism of perception that helps to parse continuous input into discrete segments on the basis of distributed temporal and spatial regularities. Nearly all previous studies of temporal statistical learning have used fixed segment lengths, e.g. composing a continuous stream of input out of three-item ‘triplets’. In natural environments, however, the units of perceptual experience — e.g. events and words — rarely come in fixed lengths. As such, we explored the operation of statistical learning in temporal streams containing segments of variable lengths. Observers passively viewed a continuous stream of novel shapes appearing one at a time. These streams had no overt segmentation, but they were constructed from segments of either a fixed length (all triplets) or variable lengths (combining one-, two-, and three-shape subsequences) — controlling for both overall stream duration and segment frequency. Statistical learning was then assessed with a two-alternative forced-choice familiarity test pitting subsequences from the stream against recombinations of those same shapes into novel subsequences. The resulting learning was equally robust for observers who had viewed fixed-length segments and observers who had viewed variable-length segments, demonstrating that statistical learning does not assume that “one-size-fits-all”. This sort of variability is not only characteristic of the visual environment, but is also a basic property of language. Accordingly, we also replicated these studies (with similar results) in auditory statistical learning of pseudowords. In other studies, we have explored the ability of both visual and auditory statistical learning to cope with other forms of variability, such as when individual elements are ‘recycled’ into multiple subsequences in the same stream — since the same objects are present in multiple events and the same syllables are used in multiple words. These results contribute to a growing body of research demonstrating the usefulness of statistical learning for everyday perception.